# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ElliotV8_original_ichiv3_roi.py

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame

import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, \
  CategoricalParameter
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real  # noqa
import technical.indicators as ftt


buy_params = {
  "base_nb_candles_buy": 61,  
  "ewo_high": 3.554,  
  "ewo_low": -19.385,  
  "low_offset": 0.987,
  "rsi_buy": 38,  
}

sell_params = {
  "base_nb_candles_sell": 43, 
  "high_offset": 0.998, 
  "high_offset_2": 1.097,
}


def EWO(dataframe, ema_length=5, ema2_length=3):
  df = dataframe.copy()
  ema1 = ta.EMA(df, timeperiod=ema_length)
  ema2 = ta.EMA(df, timeperiod=ema2_length)
  emadif = (ema1 - ema2) / df['close'] * 100
  return emadif


class Github_remiotore_freqtrade__ElliotV8_original_ichiv3_roi__20260111_210550(IStrategy):
  class HyperOpt:

      def stoploss_space(self):
          return [SKDecimal(-0.05, -0.01, decimals=3, name='stoploss')]

  INTERFACE_VERSION = 2
  """

  minimal_roi = {
      "0": 0.08,
      "20": 0.04,
      "40": 0.032,
      "87": 0.016,
      "201": 0,
      "202": -1
  }
  """

  @property
  def protections(self):
      return [
          {
              "method": "CooldownPeriod",
              "stop_duration_candles": 5
          },
          {
              "method": "MaxDrawdown",
              "lookback_period_candles": 48,
              "trade_limit": 20,
              "stop_duration_candles": 4,
              "max_allowed_drawdown": 0.2
          },
          {
              "method": "StoplossGuard",
              "lookback_period_candles": 24,
              "trade_limit": 4,
              "stop_duration_candles": 2,
              "only_per_pair": False
          },
          {
              "method": "LowProfitPairs",
              "lookback_period_candles": 6,
              "trade_limit": 2,
              "stop_duration_candles": 60,
              "required_profit": 0.02
          },
          {
              "method": "LowProfitPairs",
              "lookback_period_candles": 24,
              "trade_limit": 4,
              "stop_duration_candles": 2,
              "required_profit": 0.01
          }
      ]

  minimal_roi = {
      "0": 0.99,
      "200": -1
  }

  stoploss = -0.291

  base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
  base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
  low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True)
  high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
  high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True)

  fast_ewo = 50
  slow_ewo = 200
  ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True)
  ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True)
  rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)

  trailing_stop = True
  trailing_stop_positive = 0.001
  trailing_stop_positive_offset = 0.02
  trailing_only_offset_is_reached = True

  use_sell_signal = True
  sell_profit_only = True
  sell_profit_offset = 0.01
  ignore_roi_if_buy_signal = False

  order_time_in_force = {
      'buy': 'gtc',
      'sell': 'gtc'
  }

  timeframe = '5m'
  inf_1h = '1h'

  process_only_new_candles = True
  startup_candle_count = 400

  plot_config = {
      'main_plot': {
          'ma_buy': {'color': 'orange'},
          'ma_sell': {'color': 'orange'},
      },
  }

  def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

      for val in self.base_nb_candles_buy.range:
          dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)

      for val in self.base_nb_candles_sell.range:
          dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)

      dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)

      dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)

      dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)

      dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
      dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
      dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)

      return dataframe

  def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
      conditions = []

      conditions.append(
          (

                  (dataframe['rsi_fast'] < 35) &
                  (dataframe['close'] < (
                              dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                  (dataframe['EWO'] > self.ewo_high.value) &
                  (dataframe['rsi'] < self.rsi_buy.value) &
                  (dataframe['volume'] > 0) &
                  (dataframe['close'] < (
                              dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))

          )
      )

      conditions.append(
          (

                  (dataframe['rsi_fast'] < 35) &
                  (dataframe['close'] < (
                              dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
                  (dataframe['EWO'] < self.ewo_low.value) &
                  (dataframe['volume'] > 0) &
                  (dataframe['close'] < (
                              dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))

          )
      )

      if conditions:
          dataframe.loc[
              reduce(lambda x, y: x | y, conditions),
              'buy'
          ] = 1

      return dataframe

  def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
      conditions = []

      conditions.append(
          ((dataframe['close'] > dataframe['hma_50']) &
           (dataframe['close'] > (
                       dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) &
           (dataframe['rsi'] > 50) &
           (dataframe['volume'] > 0) &
           (dataframe['rsi_fast'] > dataframe['rsi_slow'])

           )
          |
          (
                  (dataframe['close'] < dataframe['hma_50']) &
                  (dataframe['close'] > (
                              dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
                  (dataframe['volume'] > 0) &
                  (dataframe['rsi_fast'] > dataframe['rsi_slow'])
          )

      )

      if conditions:
          dataframe.loc[
              reduce(lambda x, y: x | y, conditions),
              'sell'
          ] = 1

      return dataframe